Mobile, tablet, and wearable devices include embedded MEMS sensors like accelerometers, barometers, gyroscopes, magnetometers, and microphones. These sensors can be used by software executed within the device to determine the human motion activity being performed or undertaken by the user. The detection of such motion activities can be used by software to assist with human-machine interaction. Indeed, motion activity aware devices may minimize or even eliminate the need for human input in certain applications involving motion activities. For example, an application may, if the human user is jogging, automatically and without the need for user input, start playing music and switch on health monitoring applications. Thus, the correct detection of human motion activities may be valuable and commercially desirable, particularly in the domains of ubiquitous computing, human machine interaction, and the internet of things.
Typically, such electronic devices use generalized classification techniques to determine which motion activity, such as walking or jogging, is being performed. These generalized classification techniques are not specific to any one user, and are instead designed to provide acceptable performance for any user. However, each person has a different gait when performing such motion activities, meaning that the generalized classification techniques may be more accurate for certain users than for other users. This is compounded by the large differences in human size, shape, and weight, as sensor output for a jogging motion performed by a tall and heavy individual may be substantially different than sensor output for a jogging motion performed by a short and light individual, for example.
Therefore, to provide for better and more accurate motion activity detection and classification, the development of classification techniques that can take into account the different characteristics of different users is needed.